6 results about "Classification types" patented technology

The utility model discloses a classification type living body specimen storage device, which relates to the technical field of living body specimen storage and comprises a culture box body, a box door is mounted on the front side of the culture box body, side plates are symmetrically connected to the inner wall of the culture box body, and classification plates are arranged on the sides, close to each other, of the side plates. By arranging the limiting grid plate and the vertical rods, in the using process, the limiting grid plate and the vertical rods are matched with each other, so that a worker can conveniently limit and fix a container placed above the classification plate, and the situation that the container falls off in the drawing process of the classification plate is effectively prevented; the safety of the device in the using process can be effectively guaranteed, a connecting strip and a sliding block are arranged, when a container borne above a classification plate is taken, a worker can transversely push a limiting grid plate, so that the limiting grid plate and the classification plate are staggered, the space for the worker to take the container can be increased, and the working efficiency is improved. Therefore, the convenience of the device in the using process can be effectively improved.

The present disclosure provides a question analysis method and device, electronic equipment and storage medium, relates to the technical field of Internet, and particularly relates to the field of knowledge graph. The specific implementation scheme is: obtaining a question input by a user; performing knowledge classification on the question according to a pre-constructed classification type to obtain classification information of the question; performing knowledge understanding on the question to obtain subject segment understanding information and predicate segment understanding information of the question; and generating final analysis information of the question based on the subject segment understanding information and the predicate segment understanding information and the classification information. The embodiments of the present application can provide a general analysis scheme for knowledge class Query, can be applied in different scenes with knowledge class demand, and have stronger explainability and higher accuracy.

The invention discloses a semantic retrieval method and device. The method comprises the steps that retrieval content is input into an atomic condition recognition model to generate an atomic condition information table; inputting the retrieval content semantic information into an external general large model to obtain an expression pseudo code; inputting the atomic conditions, the retrieval content and the expression pseudo code into an external general large model to obtain atomic conditions with classification types, and extracting and assembling according to semantic information to obtain a relationship among the atomic conditions; extracting retrieval elements from the semantic information of the retrieval content; classifying the expanded entity elements to obtain classification limiting conditions, and fusing the classification limiting conditions into an atomic condition information table to obtain an updated atomic condition relationship again; according to different application requirements and the expanded retrieval entity elements, a triple model, a quadruple model or a multi-tuple model is established to form a vector model; and according to a relationship between the vector model and the updated atomic condition, performing fusion retrieval to obtain a retrieval result. According to the method, semantic retrieval can be efficiently and accurately carried out.

The invention relates to the technical field of chip verification, in particular to a verification platform configuration method, which comprises the following steps: acquiring N hardware input ports related to a current verification target in a tested design; obtaining a preset classification strategy of the verification platform and M interface sets configured according to the classification strategy; dividing the N hardware input ports into M port classification sets according to a classification strategy, and binding and connecting the hardware input ports in the M port classification sets and the interfaces in the M interface sets of the verification platform; binding an independent special input agent for each interface set in the verification platform; when the verification sequence is started each time, the transaction generated by the verification sequence is obtained, according to the classification type of data in the transaction, the verification sequence is connected to the sequencer of the special agent matched with the classification type, then excitation is applied to the hardware input port through the driver so as to drive the tested design, and universality of the verification platform is achieved.

This specification relates to the field of computer technology and provides a method and apparatus for merging and processing unstructured data. The method includes: acquiring multiple unstructured data sets to be processed; flattening the multiple unstructured data sets using preset data identifiers to obtain multiple flattening results; comparing the multiple flattening results to obtain difference information between them; classifying the difference information to obtain classification types; and merging the multiple flattening results using a preset processing strategy corresponding to the classification types, with a preset flattening result among the multiple flattening results as the standard. Through the embodiments of this specification, refined merging of unstructured data can be achieved.

This application discloses a training method, intrusion detection method, and apparatus for an intrusion detection model. It involves collecting production process data from an industrial control system, using this data as training data. Feature extraction is performed on the production process data to obtain production process feature data. This feature data, along with random noise, is input into a generative adversarial network (GAN) to obtain adversarial example data. To further improve the quality of the adversarial example data, it undergoes weighting and random projection processing to obtain target adversarial example data. This target adversarial example data is then input into the intrusion detection model to obtain its predicted classification type. Subsequently, the intrusion detection model can be trained based on the predicted classification type and type label of the target adversarial example data. Thus, training the intrusion detection model with high-quality adversarial example data improves its robustness and accuracy.